Early Strokes Are Associated with More Global Cognitive Deficits in Adults with Sickle Cell Disease
Bibliographic record
Abstract
This study sought to link neurocognitive profiles in sickle cell disease (SCD) patients with clinical characteristics. We conducted a prospective cohort study of adults with SCD who underwent comprehensive neuropsychological assessment at the UMGGR clinic at Henri Mondor Hospital, Créteil (France). A cluster analysis was performed based on neuropsychological testing scores. The association between clusters and clinical profiles was assessed. Between 2017 and 2021, 79 patients with a mean age of 36 [range 19–65] years were included. On principal component analysis, a 5-factor model presented the best fit (Bartlett’s sphericity test [χ2 (171) = 1345; p < 0.001]), explaining 72% of the variance. The factors represent distinct cognitive domains and anatomical regions. On hierarchical classification, three clusters emerged. Cluster 1 (n = 24) presented deficits in all five factors compared to Cluster 3 (n = 33). Cluster 2 (n = 22) had deficits in all factors, but to a lesser extent than Cluster 1. MoCA scores mirrored the severity of these cognitive deficits. Age, genotype and stroke prevalence did not differ significantly between clusters. However, the time of first stroke occurrence differed significantly between Cluster 1 and 2–3: 78% of strokes occurred during childhood, whereas 80% and 83% occurred during adulthood in Clusters 2 and 3, respectively. Educational attainment was also reduced in Cluster 1. SCD patients with childhood stroke seem to be at increased risk of a global cognitive deficit profile. In addition to existing methods of primary and secondary stroke prevention, early neurorehabilitation should be prioritized in order to reduce the long-term cognitive morbidity of SCD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".